Point forecasts of the price of crude oil: an attempt to “beat” the end-of-month random-walk benchmark
Nima Nonejad ()
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Nima Nonejad: Nordea and CREATES
Empirical Economics, 2024, vol. 67, issue 4, No 6, 1497-1539
Abstract:
Abstract The study of Ellwanger and Snudden (J Bank Financ 154:106962, 2023) discovers a new and remarkable finding regarding the ability of the random-walk model using the end-of-month price of crude oil to forecast future monthly average crude oil prices out-of-sample. The magnitude and nature of the relative predictive gains lead the authors to question whether any other model can “beat” the end-of-month price random-walk out-of-sample. I make an attempt to do so by relying on plain end-of-month crude oil price autoregressive fractionally integrated moving average (ARFIMA) models. These models are more nuanced and at the same time comprehensively account for one of the most salient features of the price of crude oil, namely, its persistence. Consequently, a forecaster is inclined to believe that they might “beat” the end-of-month random-walk model. However, out-of-sample results demonstrate that a uniform (definitive) conclusion cannot be drawn. On the contrary, conclusions depend heavily on the definition of “beating”, i.e. population-level versus finite-sample relative predictability, the forecast horizon, state of the business cycle and the choice of the crude oil price series itself. The decisions, judgments and dilemmas faced by the forecaster are presented and elaborated.
Keywords: Crude oil price; Fractional integration; Out-of-sample relative predictability; Random-walk model (search for similar items in EconPapers)
JEL-codes: C1 C22 C43 C53 G1 Q47 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00181-024-02599-8
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